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Results 1 - 10 of 14 for y_reshape (0.3 sec)
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tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/reshape.mlir
// Confirm we can extract type info from reshape func.func @main() -> tensor<2x2xf32> { // CHECK: %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32> // CHECK: %{{.*}} = "tfl.reshape"(%{{.*}}, %[[cst]]) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32> %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 730 bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
func.return %3 : tensor<2x1xf32> } // CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32> // CHECK: [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32> // CHECK: [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/legacy_reshape.json
// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32> // CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32> { "version": 3, "operator_codes": [ { "builtin_code": "RESHAPE" } ], "subgraphs": [ { "tensors": [ { "shape": [1, 4], "name": "input",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 986 bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/optimize.cc
auto cst_attr = rewriter.getI64TensorAttr(values); return rewriter.create<TF::ConstOp>(location, cst_attr.getType(), cst_attr); } // Rewrites broadcast->reshape to a reshape->broadcast that reduces // the rank of the input and output of the broadcast. class SimplifyBroadcastReshape : public OpRewritePattern<BroadcastToOp> { using OpRewritePattern<BroadcastToOp>::OpRewritePattern;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize_jax_random.mlir
// CHECK: } func.func @tfl_wrapped_jax_random_normal(%arg0: tensor<2xui32>) -> tuple<tensor<3x4xf32>> { // This is a fake jax random normal body. %0 = stablehlo.constant dense<0.0> : tensor<12xf32> %1 = "stablehlo.reshape"(%0) : (tensor<12xf32>) -> tensor<3x4xf32> %2 = "stablehlo.tuple"(%1) : (tensor<3x4xf32>) -> tuple<tensor<3x4xf32>> func.return %2 : tuple<tensor<3x4xf32>> } // CHECK-LABEL: func @tfl_wrapped_jax_random_uniform(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/utils.td
def IsTransposeTrivial : Constraint<CPred< "TFL::IsTransposeTrivial($0.getType().cast<ShapedType>().getShape(), $1)">>; // Constraint that checks if the reshape op is equivalent to a transpose op. // This is true if the reshape op is a trivial reshape op, meaning no change in // the order of non-identity dimensions. def IsReshapeEquivalentToTranspose : Constraint<CPred< "TFL::IsReshapeEquivalentToTranspose("
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 4.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/device-transform-nnapi.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/BUILD
) # Bundle together all the files that are used by the non-mlir file-based tests. filegroup( name = "extra_files", srcs = glob( [ "**/importer_test_min_max.cc.mlir", "**/reshape.mlir", ], ), ) # A binary to inject min/max to a tflite model. # A file check command is used to verify the imported result from this # binary format. tf_native_cc_binary(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/device-transform-nnapi.mlir
// CHECK: %[[CONCAT:.*]] = "tfl.concatenation"(%arg0, %arg1) <{axis = 0 : i32, fused_activation_function = "NONE"}> : (tensor<1xf32>, tensor<1xf32>) -> tensor<2xf32> // CHECK: %[[VAL_1:.*]] = "tfl.reshape"(%[[CONCAT]], %[[VAL_0]]) : (tensor<2xf32>, tensor<2xi32>) -> tensor<2x1xf32> // CHECK: return %[[VAL_1]] }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_no_verify.mlir
%0 = "tfl.fully_connected"(%arg0, %cst_0, %cst_1) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<4x1024x1024xbf16>, tensor<4096x1024xbf16>, none) -> tensor<4096x4096xbf16> %1 = "tfl.reshape"(%0, %cst_2) : (tensor<4096x4096xbf16>, tensor<3xi32>) -> tensor<4x1024x4096xbf16> func.return %1 : tensor<4x1024x4096xbf16>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5.8K bytes - Viewed (0)